Quantile regression: applications and current research areas
Abstract
Quantile regression offers a more complete statistical model than mean regression and now has widespread applications. Consequently, we provide a review of this technique. We begin with an introduction to and motivation for quantile regression. We then discuss some typical application areas. Next we outline various approaches to estimation. We finish by briefly summarizing some recent research areas.
Citation
Yu, Keming and Lu, Zudi and Stander, J. 2009. Quantile regression: applications and current research areas. Journal of the Royal Statistical Society, Series D. 52 (3): pp. 331-350.
Source Title
Journal of the Royal Statistical Society, Series D
Additional URLs
ISSN
Faculty
School of Science and Computing
Department of Mathematics and Statistics
Faculty of Science and Engineering
Collection
Related items
Showing items related by title, author, creator and subject.
-
Mulyani, Sri; Andriyana, Y.; Sudartianto (2017)© 2017 Author(s). Mean regression is a statistical method to explain the relationship between the response variable and the predictor variable based on the central tendency of the data (mean) of the response variable. The ...
-
Yu, K.; Lu, Zudi (2004)We consider non-parametric additive quantile regression estimation by kernel-weighted local linear fitting. The estimator is based on localizing the characterization of quantile regression as the minimizer of the appropriate ...
-
McGarry, Sarah; Burrows, S.; Ashoorian, T.; Pallathil, T.; Ong, K.; Edgar, D.; Wood, F. (2016)© 2016 Elsevier Ltd and ISBI. All rights reserved. The purpose of this study was to investigate the relationship between mental health and self-reported itch in patients with burns across a 6 month time period and to test ...